All trainable weights were initialized using the Glorot initialization scheme [1]}. In our experiments, the Adam optimizer [2]} with a batch size of 64, a learning rate of 0.001 and early stopping yielded the best results. The fusion networks were trained for 20 epochs, with early stopping triggering in the first five epochs with a patience of 4. The audio tracking networks were trained for 100 epochs, with early stopping triggering after 30 epochs with a patience of 20.
